I will be on the 2020-21 finance job market.
My research interests include studying financial markets in the large and in the small.
I maintain a public Call For Papers calendar that might interest you.
My wife and I enjoy spending our free time entertaining our son and getting outside when we can. Things I want to brag about but decided against putting on my CV: (1) completed multiple marathons, (2) won a few amateur ballroom dancing awards (credit due to my beautiful dancing partner), and (3) good listener.
I propose a way to model the cash flows in consumption-based asset pricing models that allows cash flows to have dynamic properties distinct from that of consumption. The method models dividends as the residual piece of consumption following labor income. The modification is parsimonious in that it adds only 4 parameters, it relies on observable economic moments as opposed to unobservable latent state variables, and it is economically motivated. When embedded into existing asset pricing models, the models are able to reproduce a number of salient facts about macroeconomic series, including the term structures of growth rate volatility and co-movement. The approach provides richer asset pricing dynamics and also sheds light on the role that non-financial wealth plays in the pricing of financial assets.
We use options and return data to decompose unconditional risk premia into different parts of the return state space. In the data, the entire equity premium is attributable to monthly returns below -11.3%, but returns in the extreme left tail matter very little. In contrast, leading asset pricing models based on habits, long-run risks, and rare disasters attribute the premium almost exclusively to returns above -11.3%, or to the extreme left tail. We find that model extensions with a larger quantity of tail risk cannot account for the data, while models with a higher price of tail risk can.
We study the anatomy of four widely used institutional trading algorithms representing $675 billion in demand from 961 institutions between 2012 and 2016. Parent orders generate hundreds of child orders which strategically employ price, time-in-force, and display priority rules to navigate the tradeoff between the desire to trade and minimizing transaction costs. Child orders incur price impact at the time they are submitted to the book regardless of whether or not they are (ex post) filled, and even when passively priced relative to the prevailing quote. The intra-parent distribution of child orders is non-random, generating strategic runs which oscillate between the aggressive or passive side of the spread. Despite algorithmic attempts to reduce their influence, programmatic child-level price, time-in-force, and display choices aggregate up to parent-level trading costs borne by investors.
I study the distributional properties of household risky shares, the fraction of their financial portfolio allocated to risky assets. Many proposed solutions to bring household life-cycle portfolio choice models in line with the average risky share, such as participation costs or differences in labor income risk profiles, fall far short of generating sufficient cross-sectional heterogeneity in portfolio allocations at nearly every point in the life-cycle.
I provide a tractable way to evaluate the moments and risk exposures of future financial wealth. The analysis operates under minimal assumptions about the financial portfolio returns and does not require the use of Monte Carlo simulations or assumptions about investor risk preferences. Specific applications include saving for retirement or college expenses and participation in ESOPs.
We use kinetic Monte Carlo simulations to produce solutions of an agent-based, rate equation model of an informationally efficient, closed financial market. The simulations produce a crash in the market that is forewarned through the observation of a market instability from which the market temporarily recovers. The market remained in a quasi-stable state for a relatively large amount of time between the warning and the crash, raising the prospect that some mitigating action can be taken in time to avert the impending crash. This result has strong ramifications for the future of predicting calamitous market events, especially if some observable aspect of financial markets can be positively identified and associated with simulation parameters.
I am a proponent of open source software and transparent academic research. I am an active member of the Julia community.
In addition to contributing to existing Julia packages, I maintain a number of packages I found useful in my own work. I hope that other finance researchers and practitioners can find value in them as well.
FamaFrenchData.jl – cleanly pull data directly from the Ken French Data Library
DailyTreasuryYieldCurve.jl – efficiently pull yield curves directly from the US Treasury
FinancialPortfolios.jl – convenience package for working with portfolios
AsymmetricRisk.jl – implements a number of univariate and bivariate asymmetric risk measures (WIP)
This website is also open source.